bacteria {MASS} | R Documentation

## Presence of Bacteria after Drug Treatments

### Description

Tests of the presence of the bacteria H. influenzae in children with otitis media in the Northern Territory of Australia.

### Usage

    data(bacteria)

### Format

This data frame has 220 rows and the following columns:

y
  presence or absence: a factor with levels `n` and `y`.
ap
  active/placebo: a factor with levels `a` and `p`.
hilo
  hi/low compliance: a factor with levels `hi` amd `lo`.
week
  numeric: week of test.
ID
  subject ID: a factor.
trt
  a factor with levels `placebo`, `drug` and `drug+`, a re-coding of `ap` and `hilo`.

### Details

Dr A. Leach tested the effects of a drug on 50 children with a history of otitis media in the Northern Territory of Australia. The children were randomized to the drug or the a placebo, and also to receive active encouragement to comply with taking the drug.

The presence of H. influenzae was checked at weeks 0, 2, 4, 6 and 11: 30 of the checks were missing and are not included in this data frame.

### Source

Menzies School of Health Research 1999–2000 Annual Report pp. 18–21 (http://www.menzies.edu.au/publications/anreps/MSHR00.pdf).

### References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

### Examples

    contrasts(bacteria$trt) <- structure(contr.sdif(3),
         dimnames = list(NULL, c("drug", "encourage")))
    ## fixed effects analyses
    summary(glm(y ~ trt * week, binomial, data = bacteria))
    summary(glm(y ~ trt + week, binomial, data = bacteria))
    summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria))

    # conditional random-effects analysis
    library(survival)
    bacteria$Time <- rep(1, nrow(bacteria))
    coxph(Surv(Time, unclass(y)) ~ week + strata(ID),
          data = bacteria, method = "exact")
    coxph(Surv(Time, unclass(y)) ~ factor(week) + strata(ID),
          data = bacteria, method = "exact")
    coxph(Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID),
          data = bacteria, method = "exact")

    # PQL glmm analysis
    library(nlme)
    summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
                    family = binomial, data = bacteria))

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